{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第三章 模型搭建和评估-评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据之前的模型的建模，我们知道如何运用sklearn这个库来完成建模，以及我们知道了的数据集的划分等等操作。那么一个模型我们怎么知道它好不好用呢？以至于我们能不能放心的使用模型给我的结果呢？那么今天的学习的评估，就会很有帮助。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "加载下面的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Image\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "plt.rcParams['figure.figsize'] = (10, 6)  # 设置输出图片大小"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**任务：加载数据并分割测试集和训练集**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 模型评估是为了知道模型的泛化能力。\n",
    "* 交叉验证（cross-validation）是一种评估泛化性能的统计学方法，它比单次划分训练集和测试集的方法更加稳定、全面。\n",
    "* 在交叉验证中，数据被多次划分，并且需要训练多个模型。\n",
    "* 最常用的交叉验证是 k 折交叉验证（k-fold cross-validation），其中 k 是由用户指定的数字，通常取 5 或 10。\n",
    "* 准确率（precision）度量的是被预测为正例的样本中有多少是真正的正例\n",
    "* 召回率（recall）度量的是正类样本中有多少被预测为正类\n",
    "* f-分数是准确率与召回率的调和平均"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【思考】：将上面的概念进一步的理解，大家可以做一下总结"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#思考回答：\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 任务一：交叉验证\n",
    "* 用10折交叉验证来评估之前的逻辑回归模型\n",
    "* 计算交叉验证精度的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#提示：交叉验证\n",
    "Image('Snipaste_2020-01-05_16-37-56.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 提示4\n",
    "* 交叉验证在sklearn中的模块为`sklearn.model_selection`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 思考4\n",
    "* k折越多的情况下会带来什么样的影响？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#思考回答\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 任务二：混淆矩阵\n",
    "* 计算二分类问题的混淆矩阵\n",
    "* 计算精确率、召回率以及f-分数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【思考】什么是二分类问题的混淆矩阵，理解这个概念，知道它主要是运算到什么任务中的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#思考回答\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#提示：混淆矩阵\n",
    "Image('Snipaste_2020-01-05_16-38-26.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#提示：准确率 (Accuracy),精确度（Precision）,Recall,f-分数计算方法\n",
    "Image('Snipaste_2020-01-05_16-39-27.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 提示5\n",
    "* 混淆矩阵的方法在sklearn中的`sklearn.metrics`模块\n",
    "* 混淆矩阵需要输入真实标签和预测标签\n",
    "* 精确率、召回率以及f-分数可使用`classification_report`模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【思考】\n",
    "* 如果自己实现混淆矩阵的时候该注意什么问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#思考回答\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 任务三：ROC曲线\n",
    "* 绘制ROC曲线"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【思考】什么是ROC曲线，OCR曲线的存在是为了解决什么问题？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#思考\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 提示6\n",
    "* ROC曲线在sklearn中的模块为`sklearn.metrics`\n",
    "* ROC曲线下面所包围的面积越大越好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 思考6\n",
    "* 对于多分类问题如何绘制ROC曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#思考回答\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【思考】你能从这条OCR曲线的到什么信息？这些信息可以做什么？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#思考回答\n",
    "\n"
   ]
  }
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